The aim… to find an algorithmic basis for curiosity, and to use this to control a robot so that it can discover what is “interesting” in its interaction with the world. This field of autonomous exploration, developmental robotics, artificial curiosity, intrinsic motivation, and playful robots, is at the cutting edge of cognitive science and artificial intelligence.

Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics Abstract: Developmental robotics is a new emerging field, in which robots autonomously learn about themselves and their environment, without external teachers. While this seems a formidable challenge, this trait is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.